3D convolutional neural network-based autosegmentation improves brain metastases detection and quantification for brain stereotactic radiotherapy treatment planning

Putz F, Weissmann T, Mansoorian S, Schmidt M, Heiß R, Semrau S, Frey B, Bert C, Fietkau R (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Strahlentherapie und Onkologie Urban & Vogel

Publisher: SPRINGER HEIDELBERG

City/Town: HEIDELBERG

Pages Range: S85-S86

Conference Proceedings Title: STRAHLENTHERAPIE UND ONKOLOGIE

Authors with CRIS profile

Florian Putz Department of Radiation Oncology Thomas Weissmann Department of Radiation Oncology Sina Mansoorian Department of Radiation Oncology Manuel Schmidt Neuroradiologisches Institut Rafael Heiß Institute of Radiology Sabine Semrau Medizinische Fakultät Benjamin Frey Department of Radiation Oncology Christoph Bert Professur für Med. Strahlenphysik Rainer Fietkau Lehrstuhl für Strahlentherapie

How to cite

APA:

Putz, F., Weissmann, T., Mansoorian, S., Schmidt, M., Heiß, R., Semrau, S.,... Fietkau, R. (2021). 3D convolutional neural network-based autosegmentation improves brain metastases detection and quantification for brain stereotactic radiotherapy treatment planning. In STRAHLENTHERAPIE UND ONKOLOGIE (pp. S85-S86). HEIDELBERG: SPRINGER HEIDELBERG.

MLA:

Putz, Florian, et al. "3D convolutional neural network-based autosegmentation improves brain metastases detection and quantification for brain stereotactic radiotherapy treatment planning." Proceedings of the STRAHLENTHERAPIE UND ONKOLOGIE HEIDELBERG: SPRINGER HEIDELBERG, 2021. S85-S86.

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